基于机器视觉的煤矸识别系统设计及试验研究

为解决手工选煤、湿法选煤中存在的效率低下、劳动强度大、水资源耗费、环境污染等诸多问题。研究了基于机器视觉的煤矸识别方法，在实验室中搭建了试验平台，开发了MFC软件应用平台，实现了煤矸实时识别|选取山西西山、内蒙古和陕西神木的煤和矸石作为样本，建立了样本图像库|取420张图像作为实验样本，提取样本的灰度均值、峰值灰度、能量、熵、对比度、逆差矩6个特征进行统计和分析|采用粒子群优化算法(PSO)对支持向量机(SVM)的进行优化，并对分类器进行训练和分类测试。对特征分析的结果表明，灰度特征比为纹理特征具有更好的区分度|PSO-SVM分类器测试中，以灰度、纹理、组合特征作为输入时，其识别准确率分别为95.83%、72.92%、93.75%，结果表明以灰度特征作为输入识别效果最好。

Design and experimental research of coal and gangue recognition system based on machine vision

In order to solve the problems of manual coal preparation and wet preparation method, such as inefficiency, high labor intensity, water consumption, environmental pollution. This paper studies the method of coal gangue recognition based on machine vision, builds a test platform in the laboratory, develops an application platform of MFC software, and realizes the real-time recognition of coal gangue; selects coal and gangue from Shanxi, Inner Mongolia, Shaanxi as samples, and establishes a sample image library; takes 420 images as experimental samples, and extracts the gray mean value, peak gray value, energy, entropy, contrast and deficit of samples The six features of moment are analyzed and counted; the particle swarm optimization (PSO) algorithm is used to optimize the support vector machine (SVM), and the classifier is trained and tested. The results of feature analysis show that gray-scale features have better discrimination than texture features; in PSO-SVM classifier test, when gray-scale, texture and combined features are used as input, the recognition accuracy is 95.83%、72.92%、 and 93.75% respectively, and the results show that gray features are the best input recognition effect.